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0 前言

🔥 这两年开始毕业设计和毕业答辩的要求和难度不断提升，传统的毕设题目缺少创新和亮点，往往达不到毕业答辩的要求，这两年不断有学弟学妹告诉学长自己做的项目系统达不到老师的要求。

为了大家能够顺利以及最少的精力通过毕设，学长分享优质毕业设计项目，今天要分享的是

🚩 **基于深度学习猫狗分类 **

🥇学长这里给一个题目综合评分(每项满分5分)

• 难度系数：3分
• 工作量：3分
• 创新点：3分

1 课题背景

要说到深度学习图像分类的经典案例之一，那就是猫狗大战了。猫和狗在外观上的差别还是挺明显的，无论是体型、四肢、脸庞和毛发等等， 都是能通过肉眼很容易区分的。那么如何让机器来识别猫和狗呢？这就需要使用卷积神经网络来实现了。
本项目的主要目标是开发一个可以识别猫狗图像的系统。分析输入图像，然后预测输出。实现的模型可以根据需要扩展到网站或任何移动设备。我们的主要目标是让模型学习猫和狗的各种独特特征。一旦模型的训练完成，它将能够区分猫和狗的图像。

2 使用CNN进行猫狗分类

卷积神经网络 (CNN) 是一种算法，将图像作为输入，然后为图像的所有方面分配权重和偏差，从而区分彼此。神经网络可以通过使用成批的图像进行训练，每个图像都有一个标签来识别图像的真实性质（这里是猫或狗）。一个批次可以包含十分之几到数百个图像。

对于每张图像，将网络预测与相应的现有标签进行比较，并评估整个批次的网络预测与真实值之间的距离。然后，修改网络参数以最小化距离，从而增加网络的预测能力。类似地，每个批次的训练过程都是类似的。

3 数据集处理

猫狗照片的数据集直接从kaggle官网下载即可，下载后解压，这是我下载的数据：

相关代码

import os,shutiloriginal_data_dir = "G:/Data/Kaggle/dogcat/train"
base_dir = "G:/Data/Kaggle/dogcat/smallData"
if os.path.isdir(base_dir) == False:os.mkdir(base_dir)# 创建三个文件夹用来存放不同的数据:train,validation,test
train_dir = os.path.join(base_dir,'train')
if os.path.isdir(train_dir) == False:os.mkdir(train_dir)
validation_dir = os.path.join(base_dir,'validation')
if os.path.isdir(validation_dir) == False:os.mkdir(validation_dir)
test_dir = os.path.join(base_dir,'test')
if os.path.isdir(test_dir) == False:os.mkdir(test_dir)# 在文件中:train,validation,test分别创建cats,dogs文件夹用来存放对应的数据
train_cats_dir = os.path.join(train_dir,'cats')
if os.path.isdir(train_cats_dir) == False:os.mkdir(train_cats_dir)
train_dogs_dir = os.path.join(train_dir,'dogs')
if os.path.isdir(train_dogs_dir) == False:os.mkdir(train_dogs_dir)validation_cats_dir = os.path.join(validation_dir,'cats')
if os.path.isdir(validation_cats_dir) == False:os.mkdir(validation_cats_dir)
validation_dogs_dir = os.path.join(validation_dir,'dogs')
if os.path.isdir(validation_dogs_dir) == False:os.mkdir(validation_dogs_dir)test_cats_dir = os.path.join(test_dir,'cats')
if os.path.isdir(test_cats_dir) == False:os.mkdir(test_cats_dir)
test_dogs_dir = os.path.join(test_dir,'dogs')
if os.path.isdir(test_dogs_dir) == False:os.mkdir(test_dogs_dir)#将原始数据拷贝到对应的文件夹中 cat
fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(train_cats_dir,fname)shutil.copyfile(src,dst)fnames = ['cat.{}.jpg'.format(i) for i in range(1000,1500)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(validation_cats_dir,fname)shutil.copyfile(src,dst)fnames = ['cat.{}.jpg'.format(i) for i in range(1500,2000)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(test_cats_dir,fname)shutil.copyfile(src,dst)#将原始数据拷贝到对应的文件夹中 dog
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(train_dogs_dir,fname)shutil.copyfile(src,dst)fnames = ['dog.{}.jpg'.format(i) for i in range(1000,1500)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(validation_dogs_dir,fname)shutil.copyfile(src,dst)fnames = ['dog.{}.jpg'.format(i) for i in range(1500,2000)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(test_dogs_dir,fname)shutil.copyfile(src,dst)
print('train cat images:', len(os.listdir(train_cats_dir)))
print('train dog images:', len(os.listdir(train_dogs_dir)))
print('validation cat images:', len(os.listdir(validation_cats_dir)))
print('validation dog images:', len(os.listdir(validation_dogs_dir)))
print('test cat images:', len(os.listdir(test_cats_dir)))
print('test dog images:', len(os.listdir(test_dogs_dir)))
train cat images: 1000
train dog images: 1000
validation cat images: 500
validation dog images: 500
test cat images: 500
test dog images: 500

4 神经网络的编写

cnn卷积神经网络的编写如下，编写卷积层、池化层和全连接层的代码

conv1_1 = tf.layers.conv2d(x, 16, (3, 3), padding='same', activation=tf.nn.relu, name='conv1_1')
conv1_2 = tf.layers.conv2d(conv1_1, 16, (3, 3), padding='same', activation=tf.nn.relu, name='conv1_2')
pool1 = tf.layers.max_pooling2d(conv1_2, (2, 2), (2, 2), name='pool1')
conv2_1 = tf.layers.conv2d(pool1, 32, (3, 3), padding='same', activation=tf.nn.relu, name='conv2_1')
conv2_2 = tf.layers.conv2d(conv2_1, 32, (3, 3), padding='same', activation=tf.nn.relu, name='conv2_2')
pool2 = tf.layers.max_pooling2d(conv2_2, (2, 2), (2, 2), name='pool2')
conv3_1 = tf.layers.conv2d(pool2, 64, (3, 3), padding='same', activation=tf.nn.relu, name='conv3_1')
conv3_2 = tf.layers.conv2d(conv3_1, 64, (3, 3), padding='same', activation=tf.nn.relu, name='conv3_2')
pool3 = tf.layers.max_pooling2d(conv3_2, (2, 2), (2, 2), name='pool3')
conv4_1 = tf.layers.conv2d(pool3, 128, (3, 3), padding='same', activation=tf.nn.relu, name='conv4_1')
conv4_2 = tf.layers.conv2d(conv4_1, 128, (3, 3), padding='same', activation=tf.nn.relu, name='conv4_2')
pool4 = tf.layers.max_pooling2d(conv4_2, (2, 2), (2, 2), name='pool4')flatten = tf.layers.flatten(pool4)
fc1 = tf.layers.dense(flatten, 512, tf.nn.relu)
fc1_dropout = tf.nn.dropout(fc1, keep_prob=keep_prob)
fc2 = tf.layers.dense(fc1, 256, tf.nn.relu)
fc2_dropout = tf.nn.dropout(fc2, keep_prob=keep_prob)
fc3 = tf.layers.dense(fc2, 2, None)

5 Tensorflow计算图的构建

然后，再搭建tensorflow的计算图，定义占位符，计算损失函数、预测值和准确率等等

self.x = tf.placeholder(tf.float32, [None, IMAGE_SIZE, IMAGE_SIZE, 3], 'input_data')
self.y = tf.placeholder(tf.int64, [None], 'output_data')
self.keep_prob = tf.placeholder(tf.float32)
# 图片输入网络中
fc = self.conv_net(self.x, self.keep_prob)
self.loss = tf.losses.sparse_softmax_cross_entropy(labels=self.y, logits=fc)
self.y_ = tf.nn.softmax(fc) # 计算每一类的概率
self.predict = tf.argmax(fc, 1)
self.acc = tf.reduce_mean(tf.cast(tf.equal(self.predict, self.y), tf.float32))
self.train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(self.loss)
self.saver = tf.train.Saver(max_to_keep=1)

最后的saver是要将训练好的模型保存到本地。

6 模型的训练和测试

然后编写训练部分的代码，训练步骤为1万步

acc_list = []
with tf.Session() as sess:sess.run(tf.global_variables_initializer())for i in range(TRAIN_STEP):train_data, train_label, _ = self.batch_train_data.next_batch(TRAIN_SIZE)eval_ops = [self.loss, self.acc, self.train_op]eval_ops_results = sess.run(eval_ops, feed_dict={self.x:train_data,self.y:train_label,self.keep_prob:0.7})loss_val, train_acc = eval_ops_results[0:2]acc_list.append(train_acc)if (i+1) % 100 == 0:acc_mean = np.mean(acc_list)print('step:{0},loss:{1:.5},acc:{2:.5},acc_mean:{3:.5}'.format(i+1,loss_val,train_acc,acc_mean))if (i+1) % 1000 == 0:test_acc_list = []for j in range(TEST_STEP):test_data, test_label, _ = self.batch_test_data.next_batch(TRAIN_SIZE)acc_val = sess.run([self.acc],feed_dict={self.x:test_data,self.y:test_label,self.keep_prob:1.0})test_acc_list.append(acc_val)print('[Test ] step:{0}, mean_acc:{1:.5}'.format(i+1, np.mean(test_acc_list)))# 保存训练后的模型os.makedirs(SAVE_PATH, exist_ok=True)self.saver.save(sess, SAVE_PATH + 'my_model.ckpt')

训练结果如下：

训练1万步后模型测试的平均准确率有0.82。

7 预测效果

选取三张图片测试


可见，模型准确率还是较高的。

8 最后